In [1]:
import os

## Set directory
os.chdir('/hpc/group/pbenfeylab/CheWei/CW_data/genesys')

import networkx as nx
from genesys_evaluate_v1 import *
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import warnings
# Suppress all warning messages
warnings.filterwarnings("ignore", category=DeprecationWarning)

/hpc/group/pbenfeylab/ch416/miniconda3/envs/genesys/lib/python3.8/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm
In [2]:
## Conda Env genesys on DCC
print(torch.__version__)
print(sc.__version__) 

1.11.0
1.9.6
In [3]:
## Genes considered/used (shared among samples) 
gene_list = pd.read_csv('./gene_list_1108.csv')

Load Data¶

In [4]:
with open("./genesys_root_data.pkl", 'rb') as file_handle:
    data = pickle.load(file_handle)
    
batch_size = 2000
#dataset = Root_Dataset(data['X_test'], data['y_test'])
#loader = DataLoader(dataset,
#                         batch_size = batch_size,
#                         shuffle = True, drop_last=True)

X_all = np.vstack((data['X_train'],data['X_val'],data['X_test']))
y_all = pd.concat((data['y_train'],data['y_val'],data['y_test']))
dataset = Root_Dataset(X_all, y_all)
loader = DataLoader(dataset,
                         batch_size = batch_size,
                         shuffle = True, drop_last=True)
In [5]:
classes = ['Columella', 'Lateral Root Cap', 'Phloem', 'Xylem', 'Procambium', 'Pericycle', 'Endodermis', 'Cortex', 'Atrichoblast', 'Trichoblast']
class2num = {c: i for (i, c) in enumerate(classes)}
num2class = {i: c for (i, c) in enumerate(classes)}
In [6]:
cts = ['Atrichoblast','Trichoblast','Cortex','Endodermis','Pericycle','Procambium','Xylem','Phloem','Lateral Root Cap','Columella']
ctw = np.zeros((len(cts), 17513, 17513))
## number of cells sampled from the atlas
batch_size = 2000

Extract a sample¶

In [7]:
sample = next(iter(loader))
xo = sample['x'].to(device)
y = sample['y'].to(device)
y_label = [num2class[i] for i in y.tolist()]
In [8]:
xo.shape
Out[8]:
torch.Size([2000, 11, 17513])
In [9]:
len(y_label)
Out[9]:
2000
In [10]:
len(loader)
Out[10]:
55
In [11]:
## GRN for the transition t1 to t3
for ct in cts:
    print(ct)
    cws = np.zeros((len(loader), 17513, 17513))
    with torch.no_grad():
        for i, sample in enumerate(loader):
            x = sample['x'].to(device)
            y = sample['y'].to(device)
            y_label = [num2class[i] for i in y.tolist()]
            
            #pred_h = model.init_hidden(batch_size)
            #tfrom = model.generate_current(x, pred_h, 0).to('cpu').detach().numpy()
            #cfrom = tfrom[np.where(np.array(y_label)==ct)[0],:]
            cfrom = x[np.where(np.array(y_label)==ct)[0],1,:] 
            
            #pred_h = model.init_hidden(batch_size)
            #tto = model.generate_next(x, pred_h, 0).to('cpu').detach().numpy()   
            #cto = tto[np.where(np.array(y_label)==ct)[0],:]
            cto = x[np.where(np.array(y_label)==ct)[0],3,:]
            
            cw = torch.linalg.lstsq(torch.tensor(cfrom), torch.tensor(cto)).solution.detach().numpy()
            cws[i] = cw
    
    ## Calculate mean across number of repeats
    cwm = np.mean(cws, axis=0)
    ctw[cts.index(ct)] = cwm

Atrichoblast

/tmp/ipykernel_824824/580988479.py:21: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  cw = torch.linalg.lstsq(torch.tensor(cfrom), torch.tensor(cto)).solution.detach().numpy()

Trichoblast
Cortex
Endodermis
Pericycle
Procambium
Xylem
Phloem
Lateral Root Cap
Columella
In [12]:
# Save the array to disk
np.save('genesys_raw_ctw_t1-t3.npy', ctw)
In [13]:
ctw = np.load('genesys_raw_ctw_t1-t3.npy')
In [14]:
## Calculate z-scores
ctw_z = np.zeros((len(cts), 17513, 17513))
for i in range(len(cts)):
    if np.std(ctw[i])==0:
        ctw_z[i] = np.zeros((17513, 17513))
    else:
        ctw_z[i] = (ctw[i] - np.mean(ctw[i])) / np.std(ctw[i])
In [15]:
## Filtering based on z-scores (with no weights)
ctw_f = np.zeros((len(cts), 17513, 17513))
## z-score threshold (keep values > mean + threshold*std)
threshold=3
for i in range(len(cts)):
    ctw_f[i] = np.abs(ctw_z[i]) > threshold

Load TFs list¶

In [16]:
wanted_TFs = pd.read_csv("./Kay_TF_thalemine_annotations.csv")
In [17]:
## Make TF names unique and assign preferred names
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G33880"]="WOX9"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G45160"]="SCL27"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G04410"]="NAC78"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G29035"]="ORS1"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G02540"]="ZHD3"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G16500"]="IAA26"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G09740"]="HAG5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT4G24660"]="ZHD2"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G46880"]="HDG5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G28420"]="RLT1"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G14580"]="BLJ"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G45260"]="BIB"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G02070"]="RVN"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G28160"]="FIT"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G68360"]="GIS3"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G20640"]="NLP4"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G05550"]="VFP5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G59470"]="FRF1"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G15150"]="HAT7"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G14750"]="WER"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G75710"]="BRON"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G74500"]="TMO7"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G12646"]="RITF1"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G48100"]="ARR5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT4G16141"]="GATA17L"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G65640"]="NFL"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G62700"]="VND5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT4G36160"]="VND2"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G66300"]="VND3"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G12260"]="VND4"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G62380"]="VND6"
In [18]:
pd.Series(wanted_TFs['Name']).value_counts().head(5)
Out[18]:
Name
NAC001    1
PRE5      1
MYB118    1
MYB21     1
MYB0      1
Name: count, dtype: int64

Network Analysis¶

In [19]:
TFidx = []
for i in wanted_TFs['GeneID']:
    if i in gene_list['features'].tolist():
        TFidx.append(np.where(gene_list['features']==i)[0][0])

TFidx = np.sort(np.array(TFidx))
In [20]:
def network(i):
    ## No weights
    adj_nw = ctw_f[i]
    ## Weighted
    adj = ctw[i]*ctw_f[i]
    ## TF only
    adj = adj[np.ix_(TFidx,TFidx)]
    adj_nw = adj_nw[np.ix_(TFidx,TFidx)]
    
    ## Remove no connect 
    regidx = np.sort(np.array(pd.Series(np.where(adj_nw==True)[0]).value_counts().index[pd.Series(np.where(adj_nw==True)[0]).value_counts()>=1]))
    taridx = np.sort(np.array(pd.Series(np.where(adj_nw==True)[1]).value_counts().index[pd.Series(np.where(adj_nw==True)[1]).value_counts()>=1]))
    ## Reciprocol
    keepidx = np.sort(np.array(list(set(regidx).intersection(taridx))))
    #keepidx = np.sort(np.array(list(set(regidx).union(taridx))))
    
    TFID = np.array(gene_list['features'][TFidx])[keepidx].tolist()
    ## TF name to keep
    TFname = []
    for i in np.array(gene_list['features'][TFidx])[keepidx]:
        TFname.append(wanted_TFs['Name'][np.where(wanted_TFs['GeneID']==i)[0][0]])
        
    adj = adj[np.ix_(keepidx,keepidx)]
    
    # Create a NetworkX graph for non-directed edges
    G = nx.Graph()  # supports directed edges and allows for multiple edges between the same pair of nodes
    
    # Add nodes to the graph
    num_nodes = adj.shape[0]
    for i, name in enumerate(TFname):
        G.add_node(i, name=name)
    
    # Add edges to the graph with weights
    for i in range(num_nodes):
        for j in range(num_nodes):
            weight = adj[i, j]
            if weight != 0:
                G.add_edge(j, i, weight=abs(weight), distance=1/abs(weight))
            

    ## Measures the extent to which how close a node is to all other nodes in the network, considering the shortest paths or geodesic distances between nodes
    closeness_centrality = nx.closeness_centrality(G, distance='distance')
    ## Measures the extent to which a node that are not only well-connected but also connected to other well-connected nodes.
    eigenvector_centrality = nx.eigenvector_centrality(G)
    
    # Create a NetworkX graph for diected edges
    G = nx.MultiDiGraph()  # supports directed edges and allows for multiple edges between the same pair of nodes
    
    # Add nodes to the graph
    num_nodes = adj.shape[0]
    for i, name in enumerate(TFname):
        G.add_node(i, name=name)
    
    # Add edges to the graph with weights
    for i in range(num_nodes):
        for j in range(num_nodes):
            weight = adj[i, j]
            if weight != 0:
                G.add_edge(j, i, weight=weight)
    
    ## Measures the number of connections (edges) each node has
    degree_centrality = nx.degree_centrality(G)
    # Calculate outgoing centrality
    out_centrality = nx.out_degree_centrality(G)
    # Calculate incoming centrality
    in_centrality = nx.in_degree_centrality(G)
    ## Measures the extent to which a node lies on the shortest paths between other nodes.
    betweenness_centrality = nx.betweenness_centrality(G, weight='weight')
    
    ## Non_Reciprocal Out centrality
    # Visualize the graph
    pos = nx.spring_layout(G)  # Positions of the nodes
    
    # Node colors based on weighted betweenness centrality
    node_colors = [out_centrality[node] for node in G.nodes()]
    
    # Node sizes based on weighted betweenness centrality
    node_sizes = [out_centrality[node] * 1000 for node in G.nodes()]

    # Get the edge weights as a dictionary
    edge_weights = nx.get_edge_attributes(G, 'weight')
    edge_colors = ['red' if weight > 0 else 'blue' for (_, _, weight) in G.edges(data='weight')]
    
    # Scale the edge weights to desired linewidths
    max_weight = max(edge_weights.values())
    edge_widths = [float(edge_weights[edge]) / max_weight for edge in G.edges]
    
    # Draw the graph
    nx.draw(G, pos=pos, node_color=node_colors, node_size=node_sizes, with_labels=False, width=edge_widths, edge_color=edge_colors)
    # Add node labels
    labels = {node: G.nodes[node]['name'] for node in G.nodes}
    nx.draw_networkx_labels(G, pos=pos, labels=labels, font_size=8)
    
    # Add a colorbar to show the weighted betweenness centrality color mapping
    sm = plt.cm.ScalarMappable(cmap='viridis', norm=plt.Normalize(vmin=min(node_colors), vmax=max(node_colors)))
    sm.set_array([])
    plt.colorbar(sm)
    
    # Show the plot
    plt.show()
    
    dc = pd.DataFrame.from_dict(degree_centrality, orient='index', columns=['degree_centrality'])
    oc = pd.DataFrame.from_dict(out_centrality, orient='index', columns=['out_centrality'])
    ic = pd.DataFrame.from_dict(in_centrality, orient='index', columns=['in_centrality'])
    bc = pd.DataFrame.from_dict(betweenness_centrality, orient='index', columns=['betweenness_centrality'])
    cc = pd.DataFrame.from_dict(closeness_centrality, orient='index', columns=['closeness_centrality'])
    ec = pd.DataFrame.from_dict(eigenvector_centrality, orient='index', columns=['eigenvector_centrality'])
    df = pd.concat([dc,oc,ic,bc,cc,ec], axis=1)
    df.index =TFname
    df = df.sort_values('betweenness_centrality', ascending=False)
    
    return(df)
In [21]:
atri = network(0)
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In [22]:
atri
Out[22]:
degree_centrality out_centrality in_centrality betweenness_centrality closeness_centrality eigenvector_centrality
3xHMG-box2 0.405242 0.052419 0.352823 0.867151 0.000046 0.120740
DEL3 0.094758 0.050403 0.044355 0.865396 0.000023 0.035676
TMO7 0.502016 0.316532 0.185484 0.864899 0.000042 0.135102
AT1G36060 0.413306 0.207661 0.205645 0.861934 0.000039 0.121884
MYB97 0.050403 0.014113 0.036290 0.860578 0.000025 0.022705
... ... ... ... ... ... ...
GRF9 0.022177 0.004032 0.018145 0.000000 0.000023 0.013036
AT5G46910 0.010081 0.000000 0.010081 0.000000 0.000020 0.010241
DAR4 0.014113 0.000000 0.014113 0.000000 0.000023 0.011430
SWN 0.022177 0.018145 0.004032 0.000000 0.000021 0.011525
AT1G18335 0.054435 0.002016 0.052419 0.000000 0.000033 0.026380

497 rows × 6 columns

In [23]:
tri = network(1)
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In [24]:
cor = network(2)
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In [25]:
end = network(3)
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In [26]:
per = network(4)
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In [27]:
pro = network(5)
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In [28]:
xyl = network(6)
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In [29]:
phl = network(7)
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In [30]:
lrc = network(8)
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In [31]:
col = network(9)
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In [32]:
atri.columns = ['atri_degree_centrality','atri_out_centrality','atri_in_centrality','atri_betweenness_centrality','atri_closeness_centrality','atri_eigenvector_centrality']
tri.columns = ['tri_degree_centrality','tri_out_centrality','tri_in_centrality','tri_betweenness_centrality','tri_closeness_centrality','tri_eigenvector_centrality']
cor.columns = ['cor_degree_centrality','cor_out_centrality','cor_in_centrality','cor_betweenness_centrality','cor_closeness_centrality','cor_eigenvector_centrality']
end.columns = ['end_degree_centrality','end_out_centrality','end_in_centrality','end_betweenness_centrality','end_closeness_centrality','end_eigenvector_centrality']
per.columns = ['per_degree_centrality','per_out_centrality','per_in_centrality','per_betweenness_centrality','per_closeness_centrality','per_eigenvector_centrality']
pro.columns = ['pro_degree_centrality','pro_out_centrality','pro_in_centrality','pro_betweenness_centrality','pro_closeness_centrality','pro_eigenvector_centrality']
xyl.columns = ['xyl_degree_centrality','xyl_out_centrality','xyl_in_centrality','xyl_betweenness_centrality','xyl_closeness_centrality','xyl_eigenvector_centrality']
phl.columns = ['phl_degree_centrality','phl_out_centrality','phl_in_centrality','phl_betweenness_centrality','phl_closeness_centrality','phl_eigenvector_centrality']
lrc.columns = ['lrc_degree_centrality','lrc_out_centrality','lrc_in_centrality','lrc_betweenness_centrality','lrc_closeness_centrality','lrc_eigenvector_centrality']
col.columns = ['col_degree_centrality','col_out_centrality','col_in_centrality','col_betweenness_centrality','col_closeness_centrality','col_eigenvector_centrality']
In [33]:
## Indentify main regulators in each net work
tff = []
tff = tff + atri[atri['atri_betweenness_centrality']>0].index.tolist()
tff = tff + tri[tri['tri_betweenness_centrality']>0].index.tolist()
tff = tff + lrc[lrc['lrc_betweenness_centrality']>0].index.tolist()
tff = tff + cor[cor['cor_betweenness_centrality']>0].index.tolist()
tff = tff + end[end['end_betweenness_centrality']>0].index.tolist()
tff = tff + per[per['per_betweenness_centrality']>0].index.tolist()
tff = tff + pro[pro['pro_betweenness_centrality']>0].index.tolist()
tff = tff + xyl[xyl['xyl_betweenness_centrality']>0].index.tolist()
tff = tff + phl[phl['phl_betweenness_centrality']>0].index.tolist()
tff = tff + col[col['col_betweenness_centrality']>0].index.tolist()
tf_occurance = pd.DataFrame(pd.Series(tff).value_counts())
tf_occurance = tf_occurance.rename(columns={
    'count': 'tf_occurance'
})
tf_spec = pd.concat([tf_occurance, atri, tri, lrc, cor, end, per, pro, xyl, phl, col], axis=1)
tf_spec = tf_spec.fillna(0)
In [34]:
## Epidermis (atri, tri, lrc)
celltype1='atri'
celltype2='tri'
celltype3='lrc'
ts = tf_spec[tf_spec['tf_occurance']==3][[celltype1+'_betweenness_centrality', celltype2+'_betweenness_centrality', celltype3+'_betweenness_centrality', celltype1+'_out_centrality', celltype2+'_out_centrality', celltype3+'_out_centrality', celltype1+'_in_centrality', celltype2+'_in_centrality', celltype3+'_in_centrality']]
tso = (ts > 0)
ts['centrality_count'] = tso.sum(axis=1)
ts['centrality_sum'] = ts.sum(axis=1)
ts[ts['centrality_count']==9].sort_values(['centrality_count','centrality_sum'], ascending=False)
Out[34]:
atri_betweenness_centrality tri_betweenness_centrality lrc_betweenness_centrality atri_out_centrality tri_out_centrality lrc_out_centrality atri_in_centrality tri_in_centrality lrc_in_centrality centrality_count centrality_sum
ATS 0.425102 0.798927 0.030022 0.556452 0.311864 0.269912 0.207661 0.213559 0.212389 9 12.025888
AT2G36026 0.000020 0.005122 0.931649 0.040323 0.005085 0.116150 0.108871 0.033898 0.092920 9 10.334039
RAD54 0.000102 0.017482 0.464205 0.012097 0.016949 0.018805 0.086694 0.003390 0.073009 9 9.692732
AT1G05805 0.028409 0.000003 0.012162 0.167339 0.010169 0.168142 0.062500 0.011864 0.079646 9 9.540234
TFIIIA 0.002073 0.000420 0.000722 0.058468 0.008475 0.085177 0.026210 0.066102 0.039823 9 9.287469
AT2G36720 0.000257 0.000915 0.000058 0.078629 0.016949 0.036504 0.072581 0.018644 0.018805 9 9.243342
AT3G19360 0.000004 0.001706 0.000290 0.004032 0.008475 0.028761 0.122984 0.028814 0.045354 9 9.240420
WRKY7 0.002191 0.000224 0.022866 0.030242 0.016949 0.047566 0.038306 0.008475 0.036504 9 9.203325
In [35]:
## atri, tri
celltype1='atri'
celltype2='tri'
ts = tf_spec[tf_spec['tf_occurance']==2][[celltype1+'_betweenness_centrality', celltype2+'_betweenness_centrality', celltype1+'_out_centrality', celltype2+'_out_centrality', celltype1+'_in_centrality', celltype2+'_in_centrality']]
tso = (ts > 0)
ts['centrality_count'] = tso.sum(axis=1)
ts['centrality_sum'] = ts.sum(axis=1)
ts[ts['centrality_count']==6].sort_values(['centrality_count','centrality_sum'], ascending=False)
Out[35]:
atri_betweenness_centrality tri_betweenness_centrality atri_out_centrality tri_out_centrality atri_in_centrality tri_in_centrality centrality_count centrality_sum
EGL3 0.008871 0.857103 0.036290 0.174576 0.038306 0.161017 6 7.276164
WRKY9 0.002839 0.000081 0.395161 0.045763 0.008065 0.035593 6 6.487501
CRF4 0.072739 0.003914 0.171371 0.064407 0.102823 0.015254 6 6.430508
RL6 0.046974 0.003879 0.016129 0.072881 0.127016 0.003390 6 6.270269
SWI3B 0.039659 0.024653 0.008065 0.010169 0.070565 0.010169 6 6.163279
ATE2F2 0.025460 0.087318 0.002016 0.015254 0.010081 0.022034 6 6.162164
AT4G12850 0.000012 0.081192 0.038306 0.013559 0.004032 0.010169 6 6.147272
SCL21 0.000102 0.002305 0.050403 0.020339 0.020161 0.032203 6 6.125514
FHA2 0.001967 0.000006 0.044355 0.005085 0.028226 0.005085 6 6.084723
In [36]:
## Atrichoblast specific
celltype = 'atri'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[36]:
atri_betweenness_centrality atri_out_centrality atri_in_centrality centrality_count centrality_sum
MYB97 0.860578 0.014113 0.036290 3 3.910981
GL2 0.246367 0.364919 0.108871 3 3.720157
ORC1A 0.165217 0.026210 0.040323 3 3.231749
TRY 0.125521 0.139113 0.227823 3 3.492457
BNQ3 0.048424 0.312500 0.060484 3 3.421408
TTG2 0.021314 0.312500 0.038306 3 3.372120
OFP18 0.003890 0.088710 0.024194 3 3.116793
bHLH11 0.003010 0.012097 0.020161 3 3.035268
AT4G38340 0.001975 0.006048 0.139113 3 3.147137
ULT2 0.001967 0.018145 0.018145 3 3.038258
NAC044 0.001320 0.054435 0.030242 3 3.085997
LOL2 0.000587 0.036290 0.042339 3 3.079216
AT4G01350 0.000041 0.054435 0.006048 3 3.060525
KAPP 0.000041 0.034274 0.006048 3 3.040363
AT2G20280 0.000004 0.024194 0.016129 3 3.040327
AT4G31060 0.000004 0.012097 0.028226 3 3.040327
In [37]:
## Trichoblast specific
celltype = 'tri'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[37]:
tri_betweenness_centrality tri_out_centrality tri_in_centrality centrality_count centrality_sum
RSL1 0.919743 0.137288 0.127119 3 4.184150
RHD6 0.895954 0.257627 0.155932 3 4.309513
AT3G53370 0.661512 0.533898 0.216949 3 4.412359
AGL102 0.649268 0.001695 0.037288 3 3.688251
RRS1 0.571155 0.015254 0.013559 3 3.599968
ATMYC1 0.566884 0.316949 0.222034 3 4.105867
AT5G22890 0.456591 0.033898 0.011864 3 3.502354
NAC042 0.449060 0.003390 0.006780 3 3.459230
ASIL1 0.267483 0.010169 0.003390 3 3.281042
AT2G05160 0.259653 0.042373 0.001695 3 3.303721
IAA14 0.093793 0.113559 0.050847 3 3.258200
OFP13 0.083318 0.210169 0.032203 3 3.325691
NFXL2 0.067690 0.028814 0.006780 3 3.103283
KELP 0.058600 0.018644 0.022034 3 3.099278
FLD 0.041973 0.006780 0.008475 3 3.057227
PAT1 0.037890 0.057627 0.001695 3 3.097212
AT1G11490 0.031602 0.038983 0.076271 3 3.146856
WRKY65 0.028376 0.210169 0.033898 3 3.272444
MYB47 0.015407 0.030508 0.037288 3 3.083203
FIT 0.004075 0.069492 0.013559 3 3.087126
AT3G19184 0.001810 0.010169 0.003390 3 3.015369
PHE1 0.001770 0.011864 0.030508 3 3.044143
AT5G58280 0.001675 0.001695 0.035593 3 3.038963
IAA34 0.001675 0.013559 0.005085 3 3.020319
AT3G06220 0.001675 0.001695 0.008475 3 3.011844
EMB1789 0.001652 0.011864 0.005085 3 3.018601
ETL1 0.001525 0.005085 0.001695 3 3.008305
AT5G11340 0.001269 0.023729 0.074576 3 3.099574
AT1G18335 0.000869 0.018644 0.003390 3 3.022903
AT4G09100 0.000763 0.011864 0.003390 3 3.016017
SDG16 0.000670 0.003390 0.008475 3 3.012535
AT5G13780 0.000616 0.015254 0.030508 3 3.046379
AT4G31650 0.000541 0.022034 0.001695 3 3.024270
AT3G05760 0.000446 0.016949 0.011864 3 3.029260
RAP74 0.000420 0.025424 0.003390 3 3.029234
ARF19 0.000351 0.020339 0.003390 3 3.024080
AT1G06070 0.000279 0.005085 0.003390 3 3.008754
ING1 0.000253 0.003390 0.003390 3 3.007033
AT5G58900 0.000230 0.025424 0.008475 3 3.034129
NAC027 0.000164 0.005085 0.011864 3 3.017113
WRKY47 0.000150 0.015254 0.013559 3 3.028963
AT2G39000 0.000135 0.010169 0.005085 3 3.015389
AT5G06800 0.000129 0.010169 0.003390 3 3.013689
LBD26 0.000095 0.006780 0.016949 3 3.023824
AT2G10950 0.000078 0.006780 0.011864 3 3.018722
AT3G09735 0.000060 0.059322 0.096610 3 3.155993
CIP8 0.000009 0.003390 0.011864 3 3.015263
MBF1B 0.000003 0.006780 0.016949 3 3.023732
BZIP60 0.000003 0.011864 0.001695 3 3.013562
In [38]:
## LRC specific
celltype = 'lrc'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[38]:
lrc_betweenness_centrality lrc_out_centrality lrc_in_centrality centrality_count centrality_sum
PS1 0.954564 0.038717 0.055310 3 4.048590
AT1G63100 0.931057 0.007743 0.060841 3 3.999641
SUVR2 0.746025 0.021018 0.042035 3 3.809078
AT5G19490 0.698304 0.064159 0.043142 3 3.805605
AT1G31760 0.221771 0.074115 0.142699 3 3.438585
AT5G12440 0.146736 0.030973 0.063053 3 3.240762
AT3G53440 0.065795 0.024336 0.019912 3 3.110042
LDL1 0.048227 0.026549 0.023230 3 3.098005
MBD11 0.046961 0.147124 0.105088 3 3.299174
AT2G44020 0.041624 0.017699 0.016593 3 3.075916
HDG2 0.007551 0.077434 0.066372 3 3.151356
DAR7 0.005538 0.043142 0.036504 3 3.085184
AT1G50620 0.004986 0.017699 0.034292 3 3.056977
AGL17 0.003335 0.033186 0.021018 3 3.057538
BZIP17 0.001123 0.070796 0.033186 3 3.105106
JMJD5 0.001105 0.008850 0.025442 3 3.035397
EMB93 0.001094 0.008850 0.011062 3 3.021005
PYE 0.000837 0.050885 0.034292 3 3.086014
FHY3 0.000485 0.018805 0.013274 3 3.032565
AT1G69030 0.000390 0.092920 0.067478 3 3.160788
AT5G41580 0.000386 0.032080 0.024336 3 3.056802
AT4G00270 0.000368 0.021018 0.016593 3 3.037978
AT5G08630 0.000337 0.027655 0.023230 3 3.051222
AT4G34290 0.000219 0.043142 0.088496 3 3.131856
BEH1 0.000134 0.003319 0.040929 3 3.044381
GBF1 0.000126 0.034292 0.016593 3 3.051011
SUVH5 0.000081 0.013274 0.034292 3 3.047647
AT1G24040 0.000016 0.008850 0.006637 3 3.015503
AL6 0.000015 0.018805 0.015487 3 3.034307
AT1G09060 0.000007 0.038717 0.011062 3 3.049786
MYB1 0.000007 0.017699 0.011062 3 3.028768
AT1G77250 0.000004 0.040929 0.033186 3 3.074119
In [39]:
## Columella specific
celltype = 'col'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[39]:
col_betweenness_centrality col_out_centrality col_in_centrality centrality_count centrality_sum
BPC7 0.743122 0.062358 0.069161 3 3.874641
BAM7 0.712683 0.017007 0.052154 3 3.781844
AT1G26680 0.698516 0.479592 0.806122 3 4.984230
AGL20 0.485232 0.056689 0.149660 3 3.691582
AGL94 0.352337 0.073696 0.103175 3 3.529208
... ... ... ... ... ...
WRKY14 0.000004 0.112245 0.001134 3 3.113383
PTM 0.000003 0.035147 0.009070 3 3.044220
MBD1 0.000003 0.043084 0.004535 3 3.047622
NAC053 0.000001 0.089569 0.023810 3 3.113380
AT5G48560 0.000001 0.051020 0.011338 3 3.062360

78 rows × 5 columns

In [40]:
## Ground tissue
celltype1='cor'
celltype2='end'
ts = tf_spec[tf_spec['tf_occurance']==2][[celltype1+'_betweenness_centrality', celltype2+'_betweenness_centrality', celltype1+'_out_centrality', celltype2+'_out_centrality', celltype1+'_in_centrality', celltype2+'_in_centrality']]
tso = (ts > 0)
ts['centrality_count'] = tso.sum(axis=1)
ts['centrality_sum'] = ts.sum(axis=1)
ts[ts['centrality_count']==6].sort_values(['centrality_count','centrality_sum'], ascending=False)
Out[40]:
cor_betweenness_centrality end_betweenness_centrality cor_out_centrality end_out_centrality cor_in_centrality end_in_centrality centrality_count centrality_sum
ARR3 0.056141 0.004233 0.380342 0.675307 0.070513 0.121419 6 7.307954
JKD 0.086156 0.014779 0.378205 0.364256 0.188034 0.120055 6 7.151486
RVN 0.029137 0.022607 0.034188 0.103683 0.134615 0.117326 6 6.441557
ERF15 0.000146 0.003312 0.044872 0.229195 0.029915 0.088677 6 6.396116
OFP12 0.002507 0.000007 0.166667 0.087312 0.042735 0.083220 6 6.382449
BES1 0.000471 0.000144 0.202991 0.047749 0.036325 0.047749 6 6.335429
VRN1 0.000137 0.001066 0.081197 0.061392 0.049145 0.099591 6 6.292527
HAM3 0.000357 0.000671 0.143162 0.054570 0.021368 0.032742 6 6.252870
IDD4 0.009833 0.000062 0.094017 0.008186 0.010684 0.077763 6 6.200543
ARR4 0.000009 0.000002 0.029915 0.021828 0.049145 0.027285 6 6.128184
AT1G58110 0.000009 0.000006 0.014957 0.024557 0.029915 0.013643 6 6.083086
In [41]:
## Cortex specific
celltype = 'cor'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[41]:
cor_betweenness_centrality cor_out_centrality cor_in_centrality centrality_count centrality_sum
AT2G38300 0.091908 0.418803 0.102564 3 3.613275
APRR2 0.018169 0.047009 0.042735 3 3.107913
AT1G68070 0.010382 0.010684 0.047009 3 3.068074
AT2G42660 0.007604 0.275641 0.064103 3 3.347348
AL3 0.002754 0.008547 0.076923 3 3.088225
AT2G46810 0.000549 0.042735 0.004274 3 3.047558
RR3 0.000366 0.014957 0.049145 3 3.064469
ARIA 0.000352 0.017094 0.010684 3 3.028130
AT2G47890 0.000307 0.089744 0.047009 3 3.137059
AT2G36480 0.000279 0.004274 0.027778 3 3.032330
ZFN3 0.000261 0.019231 0.014957 3 3.034449
RTV1 0.000252 0.012821 0.023504 3 3.036576
AT3G24820 0.000105 0.006410 0.027778 3 3.034293
NAC003 0.000082 0.055556 0.053419 3 3.109057
AT2G40620 0.000082 0.025641 0.029915 3 3.055638
COL4 0.000082 0.038462 0.012821 3 3.051364
AT1G04850 0.000078 0.023504 0.051282 3 3.074864
AT4G16430 0.000050 0.002137 0.042735 3 3.044922
IDD7 0.000018 0.004274 0.012821 3 3.017112
ABF3 0.000009 0.019231 0.014957 3 3.034197
ATU2AF35A 0.000009 0.004274 0.032051 3 3.036334
BEH4 0.000005 0.029915 0.027778 3 3.057697
HAM2 0.000005 0.025641 0.019231 3 3.044876
AT3G10760 0.000005 0.029915 0.021368 3 3.051287
HSFC1 0.000005 0.002137 0.010684 3 3.012825
In [42]:
## Endodermis specific
celltype = 'end'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[42]:
end_betweenness_centrality end_out_centrality end_in_centrality centrality_count centrality_sum
AT3G07260 0.331902 0.001364 0.030014 3 3.363280
MYB3 0.314966 0.889495 0.383356 3 4.587817
BIB 0.304734 0.399727 0.090041 3 3.794502
SCR 0.117714 0.538881 0.259209 3 3.915804
BLJ 0.026637 0.609823 0.118690 3 3.755150
RAX2 0.019526 0.680764 0.155525 3 3.855816
AT4G00390 0.004451 0.028649 0.035471 3 3.068571
HRS1 0.002973 0.107776 0.045020 3 3.155769
chr31 0.002842 0.169168 0.039563 3 3.211573
AT5G44080 0.002287 0.009550 0.005457 3 3.017294
EPR1 0.001444 0.012278 0.046385 3 3.060107
AT1G05710 0.001361 0.017735 0.050477 3 3.069573
AT3G56230 0.001349 0.525239 0.065484 3 3.592072
AT3G23690 0.000949 0.256480 0.091405 3 3.348834
AT3G24120 0.000686 0.060027 0.073670 3 3.134383
LSMT-L 0.000557 0.002729 0.006821 3 3.010107
TRB3 0.000145 0.013643 0.005457 3 3.019245
SIGA 0.000073 0.016371 0.005457 3 3.021901
AT4G36860 0.000006 0.031378 0.010914 3 3.042298
MYB70 0.000006 0.070941 0.031378 3 3.102325
AT5G47390 0.000002 0.057299 0.024557 3 3.081857
In [43]:
## Stele
celltype1='per'
celltype2='pro'
celltype3='xyl'
celltype4='phl'
ts = tf_spec[tf_spec['tf_occurance']==4][[celltype1+'_betweenness_centrality', celltype2+'_betweenness_centrality', celltype3+'_betweenness_centrality', celltype4+'_betweenness_centrality', celltype1+'_out_centrality', celltype2+'_out_centrality', celltype3+'_out_centrality', celltype4+'_out_centrality', celltype1+'_in_centrality', celltype2+'_in_centrality', celltype3+'_in_centrality', celltype4+'_in_centrality']]
tso = (ts > 0)
ts['centrality_count'] = tso.sum(axis=1)
ts['centrality_sum'] = ts.sum(axis=1)
ts[ts['centrality_count']==12].sort_values(['centrality_count','centrality_sum'], ascending=False)
Out[43]:
per_betweenness_centrality pro_betweenness_centrality xyl_betweenness_centrality phl_betweenness_centrality per_out_centrality pro_out_centrality xyl_out_centrality phl_out_centrality per_in_centrality pro_in_centrality xyl_in_centrality phl_in_centrality centrality_count centrality_sum
MYB20 0.875978 0.952348 0.772683 0.738637 0.481808 0.682472 0.401156 0.066735 0.682470 0.453006 0.197688 0.091376 12 18.396357
AT3G43430 0.626868 0.402482 0.431843 0.103983 0.642778 0.816257 0.227746 0.690965 0.683572 0.820491 0.149133 0.547228 12 18.143347
BT1 0.837538 0.827188 0.124560 0.000097 0.190739 0.500423 0.442775 0.053388 0.195149 0.370872 0.132948 0.128337 12 15.804013
TMO6 0.022282 0.888108 0.009281 0.481533 0.384785 0.383573 0.010405 0.471253 0.059537 0.337849 0.031214 0.436345 12 15.516164
MYB43 0.880835 0.615568 0.248281 0.001281 0.310915 0.450466 0.025434 0.062628 0.404631 0.245555 0.027746 0.062628 12 15.335966
HB40 0.980696 0.126896 0.581119 0.129236 0.038589 0.036410 0.284393 0.598563 0.033076 0.033870 0.099422 0.332649 12 15.274918
AT1G69580 0.002094 0.882927 0.878708 0.093025 0.136714 0.290432 0.093642 0.391170 0.041896 0.262489 0.025434 0.175565 12 15.274097
IAA12 0.518888 0.193477 0.929888 0.002040 0.013230 0.444539 0.527168 0.065708 0.026461 0.212532 0.139884 0.071869 12 15.145683
UNE12 0.239265 0.654097 0.016292 0.000077 0.259096 0.475868 0.183815 0.086242 0.092613 0.414903 0.083237 0.111910 12 14.617415
AT3G20640 0.276767 0.991015 0.318920 0.064772 0.085998 0.159187 0.045087 0.162218 0.087100 0.212532 0.030058 0.044148 12 14.477801
ABO3 0.028313 0.584210 0.000190 0.104982 0.210584 0.637595 0.030058 0.024641 0.013230 0.592718 0.100578 0.054415 12 14.381515
AT5G50010 0.236114 0.165396 0.449643 0.207738 0.076075 0.297206 0.247399 0.083162 0.218302 0.114310 0.087861 0.079055 12 14.262261
AT5G07580 0.001197 0.638512 0.064506 0.233510 0.208379 0.413209 0.028902 0.083162 0.009923 0.325148 0.054335 0.165298 12 14.226082
AT5G09460 0.001488 0.016995 0.987406 0.727826 0.026461 0.074513 0.073988 0.113963 0.019846 0.029636 0.072832 0.064682 12 14.209637
IDD16 0.001094 0.000002 0.883707 0.849053 0.009923 0.044030 0.089017 0.071869 0.036384 0.006774 0.080925 0.122177 12 14.194954
DAG2 0.978845 0.043051 0.001169 0.002734 0.056229 0.254022 0.021965 0.193018 0.131202 0.181202 0.012717 0.102669 12 13.978825
AT4G24060 0.026967 0.018504 0.015830 0.002719 0.169791 0.509738 0.188439 0.161191 0.160970 0.347163 0.114451 0.214579 12 13.930343
TCP14 0.000007 0.766119 0.026283 0.006519 0.023153 0.392041 0.168786 0.209446 0.026461 0.177815 0.053179 0.079055 12 13.928865
IAA13 0.000762 0.508131 0.000150 0.004198 0.164278 0.270110 0.141040 0.291581 0.040794 0.259949 0.086705 0.120123 12 13.887821
NF-YB2 0.001123 0.098624 0.872031 0.000026 0.042999 0.161727 0.310983 0.077002 0.120176 0.085521 0.019653 0.077002 12 13.866868
AT2G29660 0.004602 0.020002 0.862497 0.002656 0.046307 0.142252 0.220809 0.139630 0.097023 0.082981 0.094798 0.095483 12 13.809040
AT1G64620 0.975711 0.034860 0.052893 0.019924 0.108049 0.032176 0.100578 0.061602 0.170893 0.034716 0.019653 0.041068 12 13.652123
ASL9 0.000235 0.050507 0.740547 0.434052 0.001103 0.059272 0.132948 0.048255 0.002205 0.013548 0.116763 0.037988 12 13.637421
IAA11 0.039469 0.482266 0.001281 0.091573 0.223815 0.126164 0.024277 0.268994 0.049614 0.128704 0.084393 0.108830 12 13.629380
MYB4 0.001615 0.979706 0.004556 0.000101 0.074972 0.156647 0.045087 0.026694 0.011025 0.177815 0.052023 0.034908 12 13.565149
TCP20 0.316295 0.000095 0.603535 0.001424 0.026461 0.028789 0.228902 0.013347 0.037486 0.043184 0.025434 0.021561 12 13.346513
WOX13 0.259459 0.003673 0.016685 0.000427 0.224917 0.106689 0.050867 0.054415 0.166483 0.110076 0.110983 0.075975 12 13.180649
ZFP7 0.001700 0.000199 0.603329 0.029539 0.072767 0.060965 0.005780 0.046201 0.040794 0.054191 0.010405 0.040041 12 12.965913
RVE2 0.200279 0.014839 0.005320 0.001732 0.035281 0.171888 0.039306 0.168378 0.062845 0.116003 0.003468 0.071869 12 12.891208
AT1G12440 0.000001 0.006358 0.000019 0.000001 0.047409 0.227773 0.053179 0.067762 0.036384 0.150720 0.055491 0.049281 12 12.694379
AT3G50650 0.002295 0.114997 0.000044 0.001917 0.044101 0.076207 0.026590 0.050308 0.060639 0.060965 0.082081 0.013347 12 12.533492
SAC51 0.001432 0.003653 0.056124 0.000585 0.065050 0.113463 0.018497 0.033881 0.018743 0.113463 0.033526 0.044148 12 12.502565
In [44]:
## Pericycle
celltype = 'per'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[44]:
per_betweenness_centrality per_out_centrality per_in_centrality centrality_count centrality_sum
AT3G21330 0.879964 0.041896 0.256891 3 4.178751
IDD11 0.836492 0.209482 0.211687 3 4.257661
MDA1 0.824614 0.025358 0.014333 3 3.864305
GATA23 0.800126 0.254686 0.151047 3 4.205859
AT5G46915 0.497931 0.020948 0.052922 3 3.571801
AT4G11400 0.214800 0.005513 0.008820 3 3.229133
AT5G52010 0.097342 0.012128 0.042999 3 3.152469
AT2G47680 0.034875 0.017641 0.009923 3 3.062438
AT1G21200 0.012534 0.007718 0.013230 3 3.033483
AT4G20970 0.005086 0.108049 0.013230 3 3.126364
AT4G12240 0.003865 0.027563 0.025358 3 3.056787
CHR18 0.002328 0.033076 0.018743 3 3.054147
NF-YC13 0.001572 0.007718 0.011025 3 3.020315
AGL65 0.001437 0.014333 0.026461 3 3.042231
bHLH104 0.000388 0.060639 0.015436 3 3.076463
AT3G45880 0.000285 0.003308 0.027563 3 3.031156
AT3G56570 0.000164 0.016538 0.020948 3 3.037651
CIA2 0.000027 0.011025 0.006615 3 3.017667
DPA 0.000013 0.038589 0.017641 3 3.056243
FBH2 0.000004 0.012128 0.008820 3 3.020952
In [45]:
## Procambium
celltype = 'pro'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[45]:
pro_betweenness_centrality pro_out_centrality pro_in_centrality centrality_count centrality_sum
WRKY26 0.951384 0.104996 0.104149 3 4.160529
AT3G16940 0.884967 0.171888 0.187976 3 4.244831
DAR6 0.858827 0.060119 0.033023 3 3.951968
AT4G21060 0.822046 0.012701 0.026249 3 3.860996
AT1G78930 0.424575 0.003387 0.027942 3 3.455904
IAA32 0.400781 0.016935 0.011008 3 3.428724
VIP1 0.247219 0.021169 0.016088 3 3.284476
HB24 0.230379 0.055038 0.039797 3 3.325213
AT5G46910 0.162015 0.034716 0.022015 3 3.218747
AT4G27900 0.107202 0.012701 0.013548 3 3.133451
AT1G63490 0.105607 0.023709 0.023709 3 3.153025
WRKY45 0.062408 0.011854 0.046571 3 3.120833
AT5G24320 0.060501 0.038950 0.017782 3 3.117233
ASG3 0.049290 0.000847 0.017782 3 3.067919
AT2G24680 0.047288 0.003387 0.016935 3 3.067610
FRS5 0.037078 0.007621 0.027942 3 3.072641
SUVH9 0.019609 0.038103 0.024555 3 3.082268
WRKY2 0.018818 0.047417 0.046571 3 3.112807
FRS8 0.016059 0.015241 0.032176 3 3.063477
AMS 0.013888 0.005080 0.022862 3 3.041830
SHOT1 0.011033 0.000847 0.021169 3 3.033049
SUVH6 0.010756 0.011854 0.015241 3 3.037851
TBP2 0.009876 0.046571 0.058425 3 3.114872
FRS2 0.009101 0.055038 0.081287 3 3.145426
DUO1 0.009057 0.000847 0.003387 3 3.013290
TRB2 0.006356 0.007621 0.027942 3 3.041919
AT2G40770 0.005149 0.009314 0.008467 3 3.022930
AT3G61420 0.002186 0.020322 0.011008 3 3.033516
HK3 0.001725 0.035563 0.096528 3 3.133817
TEM1 0.001343 0.193903 0.182049 3 3.377295
WRKY4 0.001129 0.066046 0.043184 3 3.110358
TGA3 0.000880 0.001693 0.001693 3 3.004267
AT3G16350 0.000705 0.080440 0.116850 3 3.197995
BPC3 0.000519 0.007621 0.001693 3 3.009833
CDF2 0.000504 0.064352 0.091448 3 3.156305
WRKY19 0.000398 0.055885 0.091448 3 3.147730
DAR4 0.000261 0.011008 0.038103 3 3.049372
BPC1 0.000259 0.036410 0.055885 3 3.092554
ERF7 0.000205 0.090601 0.093141 3 3.183948
AT4G38070 0.000188 0.006774 0.014395 3 3.021357
LBD37 0.000170 0.064352 0.057578 3 3.122101
HAC4 0.000105 0.015241 0.002540 3 3.017886
GATA26 0.000105 0.033870 0.044877 3 3.078852
MYB95 0.000097 0.017782 0.003387 3 3.021265
AT2G37520 0.000061 0.031329 0.049958 3 3.081348
AT2G03470 0.000055 0.032176 0.051651 3 3.083883
AT4G17900 0.000055 0.079594 0.084674 3 3.164322
INO80 0.000040 0.016935 0.023709 3 3.040684
NAC083 0.000036 0.056732 0.087214 3 3.143982
ABF2 0.000029 0.027942 0.036410 3 3.064381
ERF3 0.000024 0.048264 0.058425 3 3.106713
AT1G17520 0.000004 0.026249 0.033023 3 3.059275
NF-YA9 0.000003 0.010161 0.038103 3 3.048267
OBF5 0.000003 0.025402 0.062659 3 3.088064
AT5G63080 0.000003 0.004234 0.017782 3 3.022018
RLT1 0.000003 0.014395 0.033023 3 3.047420
ZFHD1 0.000002 0.044877 0.055038 3 3.099917
In [46]:
## Xylem
celltype = 'xyl'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[46]:
xyl_betweenness_centrality xyl_out_centrality xyl_in_centrality centrality_count centrality_sum
ZHD3 0.988158 0.031214 0.065896 3 4.085268
AGL58 0.982121 0.173410 0.146821 3 4.302352
VND6 0.964376 0.057803 0.042775 3 4.064954
MYB83 0.955454 0.156069 0.092486 3 4.204009
AT5G18090 0.943056 0.065896 0.019653 3 4.028605
AT1G26610 0.938905 0.152601 0.052023 3 4.143529
VND1 0.916423 0.446243 0.132948 3 4.495614
TCP10 0.902602 0.183815 0.139884 3 4.226302
VND4 0.894953 0.364162 0.233526 3 4.492641
BZIP49 0.863843 0.041618 0.073988 3 3.979450
AT1G68200 0.809183 0.094798 0.031214 3 3.935195
VND2 0.789337 0.758382 0.373410 3 4.921129
MYB85 0.777637 0.027746 0.071676 3 3.877059
MYB46 0.720305 0.123699 0.279769 3 4.123773
VND7 0.663973 0.346821 0.216185 3 4.226979
AT5G47790 0.593808 0.003468 0.024277 3 3.621553
AT2G45120 0.458842 0.002312 0.012717 3 3.473871
MYB52 0.342654 0.023121 0.098266 3 3.464041
MYB99 0.314767 0.041618 0.042775 3 3.399160
IAA6 0.287591 0.539884 0.211561 3 4.039036
VND5 0.275015 0.273988 0.145665 3 3.694668
LBD31 0.218010 0.030058 0.113295 3 3.361363
AT5G04390 0.196654 0.008092 0.015029 3 3.219775
VND3 0.180626 0.695954 0.463584 3 4.340164
AT5G12400 0.115785 0.003468 0.065896 3 3.185149
JLO 0.109281 0.041618 0.115607 3 3.266506
SHP1 0.062455 0.114451 0.057803 3 3.234709
OFP11 0.040812 0.001156 0.003468 3 3.045436
ING2 0.019509 0.013873 0.019653 3 3.053035
AT3G49930 0.014514 0.005780 0.006936 3 3.027231
LBD18 0.013041 0.038150 0.100578 3 3.151769
AT1G67310 0.007829 0.008092 0.009249 3 3.025170
HB31 0.006378 0.020809 0.043931 3 3.071118
FAR1 0.001287 0.002312 0.006936 3 3.010536
WLIM2a 0.000578 0.258960 0.093642 3 3.353179
AT5G06770 0.000062 0.038150 0.043931 3 3.082142
ABF4 0.000039 0.039306 0.019653 3 3.058998
HB34 0.000033 0.058960 0.043931 3 3.102924
HY5 0.000031 0.013873 0.028902 3 3.042805
TLP2 0.000024 0.080925 0.055491 3 3.136440
FBH3 0.000015 0.023121 0.094798 3 3.117934
MBD02 0.000001 0.026590 0.028902 3 3.055493
AT1G03350 0.000001 0.024277 0.052023 3 3.076302
In [47]:
## Phloem
celltype = 'phl'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[47]:
phl_betweenness_centrality phl_out_centrality phl_in_centrality centrality_count centrality_sum
GATA20 0.973567 0.270021 0.251540 3 4.495127
NAC086 0.972790 0.196099 0.155031 3 4.323919
DOF6 0.946060 0.686858 0.431211 3 5.064130
HCA2 0.691290 0.496920 0.223819 3 4.412029
AT1G68030 0.675633 0.018480 0.003080 3 3.697194
AT4G37180 0.597349 0.564682 0.204312 3 4.366343
WOX2 0.571320 0.069815 0.034908 3 3.676043
PIF7 0.546462 0.010267 0.031828 3 3.588556
KAN2 0.469095 0.154004 0.147844 3 3.770943
AT1G76880 0.467731 0.004107 0.005133 3 3.476972
HAG5 0.332401 0.012320 0.023614 3 3.368335
NAC057 0.239110 0.559548 0.411704 3 4.210363
AT3G12730 0.194377 0.747433 0.659138 3 4.600947
MYB65 0.133254 0.010267 0.012320 3 3.155841
LBD25 0.132639 0.004107 0.067762 3 3.204507
AT2G03500 0.097281 0.702259 0.482546 3 4.282086
APL 0.096141 0.917864 0.894251 3 4.908256
AT1G26790 0.048754 0.068789 0.084189 3 3.201731
LDL2 0.035813 0.005133 0.019507 3 3.060454
MYB64 0.014983 0.007187 0.006160 3 3.028330
TRFL5 0.010164 0.005133 0.009240 3 3.024537
AT1G64530 0.008240 0.072895 0.041068 3 3.122203
AT3G46070 0.002971 0.036961 0.010267 3 3.050199
AT2G21230 0.002664 0.016427 0.004107 3 3.023198
bZIP19 0.001225 0.122177 0.120123 3 3.243525
ARF17 0.001077 0.049281 0.039014 3 3.089373
FRS10 0.000420 0.006160 0.006160 3 3.012740
AT2G33720 0.000213 0.002053 0.012320 3 3.014587
GT-1 0.000170 0.021561 0.030801 3 3.052531
DA1 0.000066 0.034908 0.030801 3 3.065775
ZAP1 0.000042 0.049281 0.035934 3 3.085258
HK2 0.000030 0.017454 0.029774 3 3.047257
AT5G28040 0.000022 0.044148 0.044148 3 3.088318
TLP1 0.000021 0.068789 0.014374 3 3.083183
bZIP16 0.000014 0.016427 0.018480 3 3.034921
RR2 0.000014 0.033881 0.020534 3 3.054429
AT2G18090 0.000007 0.018480 0.011294 3 3.029782
JAZ11 0.000007 0.023614 0.025667 3 3.049289
GBF2 0.000007 0.036961 0.021561 3 3.058529
MC1 0.000006 0.034908 0.035934 3 3.070848
FBH1 0.000005 0.035934 0.017454 3 3.053393
FES1 0.000004 0.023614 0.063655 3 3.087273
FRS4 0.000003 0.010267 0.017454 3 3.027724
AT1G72740 0.000001 0.044148 0.051335 3 3.095484
NF-YC3 0.000001 0.094456 0.065708 3 3.160165
In [48]:
tf_spec.to_csv('scRNAseq_TF_GRN_centrality_t1-t3_zscore3.csv', index=True)
In [49]:
tf_spec
Out[49]:
tf_occurance atri_degree_centrality atri_out_centrality atri_in_centrality atri_betweenness_centrality atri_closeness_centrality atri_eigenvector_centrality tri_degree_centrality tri_out_centrality tri_in_centrality ... phl_in_centrality phl_betweenness_centrality phl_closeness_centrality phl_eigenvector_centrality col_degree_centrality col_out_centrality col_in_centrality col_betweenness_centrality col_closeness_centrality col_eigenvector_centrality
HMGA 8.0 0.231855 0.137097 0.094758 0.015669 0.000034 0.089956 0.054237 0.010169 0.044068 ... 0.368583 0.001411 0.000444 0.090355 0.030612 0.014739 0.015873 0.000000 0.000126 0.013363
HTA2 8.0 0.207661 0.034274 0.173387 0.008207 0.000036 0.080602 0.047458 0.005085 0.042373 ... 0.147844 0.006075 0.000397 0.047665 0.056689 0.048753 0.007937 0.000000 0.000120 0.022492
H2AXA 8.0 0.294355 0.177419 0.116935 0.859185 0.000035 0.098149 0.115254 0.020339 0.094915 ... 0.104723 0.000000 0.000379 0.039862 0.126984 0.089569 0.037415 0.000642 0.000151 0.040209
HAT1 7.0 0.070565 0.020161 0.050403 0.001026 0.000028 0.036259 0.010169 0.001695 0.008475 ... 0.100616 0.000248 0.000365 0.045064 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
AT4G30410 7.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.320339 0.279661 0.040678 ... 0.045175 0.000002 0.000301 0.029961 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
AT3G22560 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
BLH3 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
ZFP8 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.010267 0.000000 0.000204 0.006205 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
AT3G52270 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.004107 0.000000 0.000180 0.006133 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
WRKY75 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.017007 0.012472 0.004535 0.000000 0.000133 0.007206

1465 rows × 61 columns

In [ ]: